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Record W4365815850 · doi:10.1111/een.13243

The effect of urbanisation and seasonality on wild bee abundance, body size and foraging efforts

2023· article· en· W4365815850 on OpenAlexafffund
Sandara N. R. Brasil, Anthony C. Ayers, Sandra M. Rehan

Bibliographic record

VenueEcological Entomology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForagingUrbanizationBiologyAbundance (ecology)EcologyHabitatBiodiversity

Abstract

fetched live from OpenAlex

Abstract Anthropogenic changes highly impact the world's biodiversity. An important human‐driven change to natural environments is increasing urbanisation, which is responsible for decreasing suitable habitats for many wild species, including bees. In this study, we investigate if three levels of urbanisation (low, medium and high) affect body size, foraging efforts and abundance of the sweat bee Agapostemon virescens . Overall, A. virescens was more abundant in medium‐urbanised sites. Second‐generation females (summer bees) were more abundant than overwintered (spring bees) at all levels of urbanisation. According to body size, female bees were larger in highly urbanised sites and male bees were larger in medium‐urbanised sites. According to foraging efforts, we observed an increase in wing wear during spring and a decrease during summer. It was also found a female‐biased sex ratio under high urbanisation and a male‐biased in low urbanisation sites. Our results suggest that highly urbanised sites can still provide sufficient nesting and foraging resources for A. virescens . In addition to our findings of higher bee abundance in low and medium urbanised sites, we suggest that maintaining different levels of urbanisation and heterogenous landscapes within a populous city might have a more positive impact on wild be sustainability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.232
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2023
Admission routes2
Has abstractyes

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